{"id":397,"date":"2021-10-14T16:43:33","date_gmt":"2021-10-14T23:43:33","guid":{"rendered":"https:\/\/faculty.engineering.asu.edu\/michelusi\/?p=397"},"modified":"2021-10-18T07:57:28","modified_gmt":"2021-10-18T14:57:28","slug":"new-jsac-paper-accepted-2","status":"publish","type":"post","link":"https:\/\/faculty.engineering.asu.edu\/michelusi\/2021\/10\/new-jsac-paper-accepted-2\/","title":{"rendered":"New JSAC paper accepted!"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Our paper &#8220;<em><em>Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale<a href=\"https:\/\/edas.info\/showPaper.php?m=1570758329\" data-type=\"URL\" data-id=\"https:\/\/edas.info\/showPaper.php?m=1570758329\"> <\/a>Variational Framework<\/em>\u00a0<\/em>&#8221; has been accepted for publication at the IEEE JSAC special issue on Machine Learning in Communications and Networks!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Co-authored by Muddassar Hussain and myself.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a learning and adaptation framework in which the dynamics of the communication beams are learned and then exploited to design adaptive beam-tracking and training with low overhead: on a long-timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training feedback to learn a probabilistic model of beam dynamics and enable predictive beam-tracking; on a short-timescale, an adaptive beam-training procedure is formulated as a partially observable (PO-) Markov decision process (MDP) and optimized via point-based value iteration (PBVI) by leveraging beam-training feedback and a probabilistic prediction of the strongest beam pair provided by the DR-VAE. In turn, beam-training feedback is used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. The proposed DR-VAE learning framework learns accurate beam dynamics: it reduces the Kullback-Leibler divergence between the ground truth and the learned model of beam dynamics by ~95% over the Baum-Welch algorithm and a naive learning approach that neglects feedback errors. Numerical results on a line-of-sight (LOS) scenario with multipath reveal that the proposed dual timescale approach yields near-optimal spectral efficiency, and improves it by 130% over a policy that scans exhaustively over the dominant beam pairs, and by 20% over a state-of-the-art POMDP policy. Finally, a low-complexity policy is proposed by reducing the POMDP to an error-robust MDP, and is shown to perform well in regimes with infrequent feedback errors.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/pdf\/2107.05466.pdf\">https:\/\/arxiv.org\/pdf\/2107.05466.pdf<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p class=\"mb-2\">Our paper &#8220;Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: a Dual Timescale Variational Framework\u00a0&#8221; has been accepted for publication at the IEEE JSAC special issue on Machine Learning in Communications and Networks! Co-authored by Muddassar Hussain and myself. Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a&#8230;<\/p>\n","protected":false},"author":112,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-397","post","type-post","status-publish","format-standard","hentry","category-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts\/397","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/users\/112"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/comments?post=397"}],"version-history":[{"count":0,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts\/397\/revisions"}],"wp:attachment":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/media?parent=397"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/categories?post=397"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/tags?post=397"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}